How to normalize a 2-dimensional numpy array in python less verbose

Question

Given a 3 times 3 numpy array  a   numpy arange 0 27 3  reshape 3 3     array    0   3   6              9  12  15             18  21  24      To normalize the rows of the 2-dimensional array I thought of  row sums   a sum axis 1    array   9  36  63   new matrix   numpy zeros  3 3   for i   row  row sum  in enumerate zip a  row sums        new matrix i      row   row sum   There must be a better way  isn t there   Perhaps to clearify  By normalizing I mean  the sum of the entrys per row must be one  But I think that will be clear to most people

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it appears that this also works  def normalizeRows M       row sums   M sum axis 1      return M   row sums

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You could use built-in numpy function   np linalg norm a  axis   1  keepdims   True

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I think you can normalize the row elements sum to 1 by this      new matrix   a   a sum axis 1  keepdims 1   And the column normalization can be done with new matrix   a   a sum axis 0  keepdims 1   Hope this can hep

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Or using lambda function  like   gt  gt  gt  vec   np arange 0 27 3  reshape 3 3   gt  gt  gt  import numpy as np  gt  gt  gt  norm vec   map lambda row  row np linalg norm row   vec    each vector of vec will have a unit norm

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Scikit-learn offers a function normalize   that lets you apply various normalizations  The  quot make it sum to 1 quot  is called L1-norm  Therefore  from sklearn preprocessing import normalize  matrix   numpy arange 0 27 3  reshape 3 3  astype numpy float64    array     0     3     6                9    12    15               18    21    24      normed matrix   normalize matrix  axis 1  norm  l1        0           0 33333333  0 66666667       0 25        0 33333333  0 41666667       0 28571429  0 33333333  0 38095238    Now your rows will sum to 1

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In case you are trying to normalize each row such that its magnitude is one  i e  a row s unit length is one or the sum of the square of each element in a row is one    import numpy as np  a   np arange 0 27 3  reshape 3 3   result   a   np linalg norm a  axis -1     np newaxis    array    0            0 4472136    0 89442719              0 42426407   0 56568542   0 70710678              0 49153915   0 57346234   0 65538554      Verifying   np sum  result  2  axis -1     array   1    1    1

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Here is one more possible way using reshape  a norm    a a sum axis 1  reshape -1 1   round 3  print a norm   Or using None works too  a norm    a a sum axis 1    None   round 3  print a norm   Output  array   0      0 333  0 667           0 25   0 333  0 417           0 286  0 333  0 381

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normed matrix   normalize input data  axis 1  norm  l1   print normed matrix    where input data is the name of your 2D array

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You could also use matrix transposition    a T   row sums  T

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Broadcasting is really good for this   row sums   a sum axis 1  new matrix   a   row sums    numpy newaxis    row sums    numpy newaxis  reshapes row sums from being  3   to being  3  1   When you do a   b  a and b are broadcast against each other   You can learn more about broadcasting here or even better here

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I think this should work   a   numpy arange 0 27  3  reshape 3 3   a     a sum axis 1    numpy newaxis

[python] How to normalize a 2-dimensional numpy array in python less verbose?

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